Energy price matrix factorization

Vassilis Kekatos

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Statistical learning tools are utilized here to study the potential risks of revealing the topology of the underlying power grid using publicly available market data. It is first recognized that the vector of real-time locational marginal prices admits an interesting decomposition: It can be expressed as the product of a sparse, positive definite matrix with non-positive off-diagonal entries times a sparse vector. A convex optimization problem involving sparse regularizers is formulated to recover the constituent factors under relevant noisy and noiseless scenarios. To tackle the high dimensionality and the streaming nature of real-time energy market data, an online algorithm with efficient closed-form iterates is developed. The grid topology matrix is updated every time a new set of locational marginal prices becomes available. Numerical tests with real demand data used on the IEEE 30-bus grid benchmark justify that the solver can partially track the underlying grid topology.

Original languageEnglish (US)
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479982974
StatePublished - Apr 24 2015
Event48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 2 2014Nov 5 2014

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/TerritoryUnited States
CityPacific Grove


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